






"""simple docstring"""





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 	a		(   __snake_case						):
    # to overwrite at feature extractactor specific tests
    SCREAMING_SNAKE_CASE		:	int		=					None
    SCREAMING_SNAKE_CASE		:	Any		=					None






    @property
    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[int]:
      return self.feat_extract_tester.prepare_feat_extract_dict()






    def   UpperCamelCase			(			self	:				Dict   )	->						Tuple:
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'feature_size'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'sampling_rate'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'padding_value'   )   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common()
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      self.assertTrue(all(len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   ) for x, y in zip(__SCREAMING_SNAKE_CASE			,		processed_features[input_name]   )   )   )

      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}			,		tensor_type='np'   )

      lowerCamelCase_       	=		processed_features[input_name]

      if len(batch_features_input.shape   ) < 3:
        lowerCamelCase_       	=		batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






    @require_torch
    def   UpperCamelCase			(			self	:				str   )	->						int:
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}			,		tensor_type='pt'   )

      lowerCamelCase_       	=		processed_features[input_name]

      if len(batch_features_input.shape   ) < 3:
        lowerCamelCase_       	=		batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






    @require_tf
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}			,		tensor_type='tf'   )

      lowerCamelCase_       	=		processed_features[input_name]

      if len(batch_features_input.shape   ) < 3:
        lowerCamelCase_       	=		batch_features_input[:, :, None]

      self.assertTrue(
          batch_features_input.shape
          == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Any=False   )	->						Optional[Any]:
      def _inputs_have_equal_length(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        lowerCamelCase_       	=		len(input[0]   )
        for input_slice in input[1:]:
          if len(__SCREAMING_SNAKE_CASE   ) != length:
            return False
        return True

      def _inputs_are_equal(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Tuple   ):
        if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
          return False

        for input_slice_a, input_slice_a in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          if not np.allclose(np.asarray(__SCREAMING_SNAKE_CASE   )			,		np.asarray(__SCREAMING_SNAKE_CASE   )			,		atol=1e-3   ):
            return False
        return True

      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      lowerCamelCase_       	=		self.feat_extract_tester.seq_length_diff
      lowerCamelCase_       	=		self.feat_extract_tester.max_seq_length + pad_diff
      lowerCamelCase_       	=		self.feat_extract_tester.min_seq_length
      lowerCamelCase_       	=		self.feat_extract_tester.batch_size
      lowerCamelCase_       	=		self.feat_extract_tester.feature_size

      # test padding for List[int] + numpy
      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[-1]   )   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='np'   )
      lowerCamelCase_       	=		input_a[input_name]

      # max_length parameter has to be provided when setting `padding="max_length"`
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='max_length'   )[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=__SCREAMING_SNAKE_CASE			,		return_tensors='np'   )
      lowerCamelCase_       	=		input_a[input_name]

      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(len(input_a[0]   ) == pad_min_length   )
      self.assertTrue(len(input_a[1]   ) == pad_min_length + pad_diff   )
      self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]   ))   )
      self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)   )

      if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size   )

      # test padding for `pad_to_multiple_of` for List[int] + numpy
      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		pad_to_multiple_of=10   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		pad_to_multiple_of=10   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		pad_to_multiple_of=10			,		max_length=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		pad_to_multiple_of=10			,		max_length=__SCREAMING_SNAKE_CASE			,		return_tensors='np'			,		)
      lowerCamelCase_       	=		input_a[input_name]

      self.assertTrue(all(len(__SCREAMING_SNAKE_CASE   ) % 10 == 0 for x in input_a   )   )
      self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

      lowerCamelCase_       	=		pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
      self.assertTrue(all(len(__SCREAMING_SNAKE_CASE   ) == expected_mult_pad_length for x in input_a   )   )
      self.assertEqual(input_a.shape[:2]			,		(batch_size, expected_mult_pad_length)   )

      if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size   )

      # Check padding value is correct
      lowerCamelCase_       	=		(np.ones(self.feat_extract_tester.feature_size   ) * feat_extract.padding_value).sum()
      self.assertTrue(
          abs(np.asarray(input_a[0]   )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   )
          < 1e-3   )
      self.assertTrue(
          abs(
              np.asarray(input_a[1]   )[pad_min_length + pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)   )
          < 1e-3   )
      self.assertTrue(
          abs(
              np.asarray(input_a[2]   )[pad_min_length + 2 * pad_diff :].sum()
              - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)   )
          < 1e-3   )
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   ) < 1e-3   )
      self.assertTrue(
          abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)   )
          < 1e-3   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=False   )	->						Union[str, Any]:
      def _inputs_have_equal_length(__SCREAMING_SNAKE_CASE	:				str   ):
        lowerCamelCase_       	=		len(input[0]   )
        for input_slice in input[1:]:
          if len(__SCREAMING_SNAKE_CASE   ) != length:
            return False
        return True

      def _inputs_are_equal(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Tuple   ):
        if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
          return False

        for input_slice_a, input_slice_a in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          if not np.allclose(np.asarray(__SCREAMING_SNAKE_CASE   )			,		np.asarray(__SCREAMING_SNAKE_CASE   )			,		atol=1e-3   ):
            return False
        return True

      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      # truncate to smallest
      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )			,		truncation=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )   )
      lowerCamelCase_       	=		input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )

      # truncate to smallest with np
      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )			,		return_tensors='np'			,		truncation=__SCREAMING_SNAKE_CASE			,		)
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )			,		return_tensors='np'   )
      lowerCamelCase_       	=		input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(input_a.shape[1] == len(speech_inputs[0]   )   )

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )

      # truncate to middle
      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[1]   )			,		truncation=__SCREAMING_SNAKE_CASE			,		return_tensors='np'			,		)
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[1]   )			,		truncation=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[1]   )			,		return_tensors='np'   )
      lowerCamelCase_       	=		input_a[input_name]

      self.assertTrue(input_a.shape[1] == len(speech_inputs[1]   )   )
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

      # since truncation forces padding to be smaller than longest input
      # function can't return `np.ndarray`, but has to return list
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertTrue(len(input_a[-1]   ) == len(speech_inputs[-1]   )   )

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        feat_extract.pad(__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE   )[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		truncation=__SCREAMING_SNAKE_CASE   )[input_name]

      # padding has to be max_length when setting `truncation=True`
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		truncation=__SCREAMING_SNAKE_CASE   )[input_name]

      # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='max_length'			,		truncation=__SCREAMING_SNAKE_CASE   )[input_name]

      # test truncation for `pad_to_multiple_of` for List[int] + numpy
      lowerCamelCase_       	=		12
      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )			,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		)
      lowerCamelCase_       	=		input_a[input_name]

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=len(speech_inputs[0]   )			,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE			,		)
      lowerCamelCase_       	=		input_a[input_name]

      # retrieve expected_length as multiple of pad_to_multiple_of
      lowerCamelCase_       	=		len(speech_inputs[0]   )
      if expected_length % pad_to_multiple_of != 0:
        lowerCamelCase_       	=		((len(speech_inputs[0]   ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

      self.assertTrue(len(input_a[0]   ) == expected_length   )
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      self._check_padding(numpify=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						List[str]:
      self._check_padding(numpify=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      self._check_truncation(numpify=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      self._check_truncation(numpify=__SCREAMING_SNAKE_CASE   )






    @require_torch
    def   UpperCamelCase			(			self	:				Any   )	->						str:
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common()
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='np'   )[input_name]
      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='pt'   )[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )






    @require_tf
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      lowerCamelCase_       	=		self.feature_extraction_class(**self.feat_extract_dict   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common()
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='np'   )[input_name]
      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='tf'   )[input_name]

      self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_tf.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )






    def   UpperCamelCase			(			self	:				Any   )	->						Any:
      lowerCamelCase_       	=		self.feat_extract_dict
      lowerCamelCase_       	=		True
      lowerCamelCase_       	=		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common()
      lowerCamelCase_       	=		[len(__SCREAMING_SNAKE_CASE   ) for x in speech_inputs]
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )

      lowerCamelCase_       	=		feat_extract.pad(__SCREAMING_SNAKE_CASE			,		padding='longest'			,		return_tensors='np'   )
      self.assertIn('attention_mask'			,		__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(list(processed.attention_mask.shape   )			,		list(processed[input_name].shape[:2]   )   )
      self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[str]:
      lowerCamelCase_       	=		self.feat_extract_dict
      lowerCamelCase_       	=		True
      lowerCamelCase_       	=		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.feat_extract_tester.prepare_inputs_for_common()
      lowerCamelCase_       	=		[len(__SCREAMING_SNAKE_CASE   ) for x in speech_inputs]
      lowerCamelCase_       	=		feat_extract.model_input_names[0]

      lowerCamelCase_       	=		BatchFeature({input_name: speech_inputs}   )
      lowerCamelCase_       	=		min(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		feat_extract.pad(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=__SCREAMING_SNAKE_CASE			,		truncation=__SCREAMING_SNAKE_CASE			,		return_tensors='np'   )
      self.assertIn('attention_mask'			,		__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(
          list(processed_pad.attention_mask.shape   )			,		[processed_pad[input_name].shape[0], max_length]   )
      self.assertListEqual(
          processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()			,		[max_length for x in speech_inputs]   )












"""simple docstring"""





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str=13			,		__SCREAMING_SNAKE_CASE	:				int=30			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=32			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=37			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCamelCase_       	=		(image_size // patch_size) ** 2
      lowerCamelCase_       	=		num_patches + 1






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      return ViTMSNConfig(
          image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		initializer_range=self.initializer_range			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      lowerCamelCase_       	=		ViTMSNModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str   )	->						List[str]:
      lowerCamelCase_       	=		self.type_sequence_label_size
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'   )
      print('Labels: {labels}'   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      lowerCamelCase_       	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	str		=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	str		=					False






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		ViTMSNModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      self.config_tester.run_common_tests()






    @unittest.skip(reason='ViTMSN does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      pass






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(model.get_input_embeddings()			,		(nn.Module)   )
        lowerCamelCase_       	=		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			,		nn.Linear   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      torch.manual_seed(2   )
      lowerCamelCase_       	=		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.0_803, -0.4_454, -0.2_375]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




